International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
3. DATA PROCESSING 
3.1 Study area and data 
The proposed approach was tested against LIDAR data 
enhanced by a list of building address points. The data was 
collected with an approximate density of 12 points per m?. The 
scene is located in the old town of Brzeg (Poland) and presents 
a market square and its surroundings (an overview of the area is 
illustrated in Fig. 4a). The size of the area is about 0.5 km. It 
comprehends 361 individual buildings that constitute 105 
adjacent building clusters. The area corresponds to a very dense 
urban settlement with various building size and shapes. 
Complex urban configuration additionally complicates the task 
of building boundary reconstruction. 
3.2 Quality assessment 
The correctness verification was performed by the comparison 
of the extraction results with the building contours obtained 
from cadastre. The quality was estimated by using area-based 
accuracy measures (Song and Haithcoat, 2005). Their indexes 
are as follows: 
= Matched overlay (the percentage of overlapping parts 
of reconstructed buildings to the total area of 
reference building regions): 90%. The overlay with 
cadastral information is illustrated by Fig. 3a. 
Area omission errors (total area of non-detected 
building parts divided by the total area of reference 
objects): 10% (marked as blue regions in Fig. 3b). 
Area commission errors (total area of incorrectly 
detected building parts divided by the total area of 
detected objects): 8% (marked as red regions in 
Fig. 3b). 
The visual check of the results reveals that the indexes are 
strongly deteriorated by improper handling of closed building 
clusters and false enforcement of regular angles. All that errors 
arise from the last reconstruction step boundary 
regularization. Therefore, application of more robust approach 
(like for example presented by Guercke and Sester, 2011) in the 
future research should significantly increase the quality of the 
whole results. 
3.3 Results and discussion 
Input data is presented in Fig. 4b. Figure 4c illustrates the 
results of building detection. The buildings are extracted based 
on their address points from the height image interpolated with 
resolution of 0.5 m. The visualisation shows that the algorithm 
provides good results. Although the gridded image facilitates 
detection process and efficient computation, its level of detail is 
deteriorated during interpolation. Hence, the image is only used 
to detect an approximate set of boundary points (real boundary 
points and outliers) from the original data. Such initially 
extracted boundaries are presented in Fig. 4d. Final results of 
the building outlines reconstruction — computed from original 
LIDAR data and adjusted — are illustrated in Fig. 4e. Figure 4f 
shows the reconstructed outlines superimposed on the 
orthophoto of the area. It is seen that the most of buildings are 
outlined very precise. Although the algorithm generally works 
promising, in some cases it returns poor results. The most 
important problem arises from the right angle constraint. From 
the visual check it might be inferred that the regularization step 
improves the shapes of more standard objects. However, when 
the objects do not feature parallelism and rectangularity the 
final results are completely corrupted. In such cases there is 
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especially hard to maintain a trade-off between the regularity 
constraint and the level of freedom. For the complex shapes 
(e.q. churches or castles) the adjustment step deteriorates initial 
boundaries. Another problem is observed for the building 
regions that contain an empty space inside. In such situation 
only the outer boundary is extracted. Finally, not individual 
buildings but their clusters are reconstructed. No automatic tool 
can determine a border between neighbouring buildings where 
there is no gap between them. However, for the clusters with 
differences in the roof structures, an improvement to the results 
could be partially achieved by analysing normal vectors in local 
neighbourhood. 
  
  
  
  
(b) 
Figure 3. Comparison with a reference data, (a) building 
footprints from cadastre (green) and reconstructed building 
outlines (black); (b) omission errors (blue) and commission 
errors (red).